Noise reduction (NR) in image sequences can improve both image quality and performance of subsequent video coding. This is so because noise in image sequences adds spurious uncorrelated image components which are visually offensive and which may reduce the effectiveness of any compression scheme which relies on image correlations from frame to frame.
As set forth in a book by J. S. Lim, entitled Two Dimensional Signal and Image Processing, Prentice Hall, 1990, pages 568 et seq., the simplest method for performing temporal filtering is through frame averaging. Frame averaging is very effective in processing a sequence of image frames which are contaminated by random noise but in which there is not much change in image information from frame to frame.
As is well known in the art, there are many different ways of performing frame averaging. Although frame averaging may be very simple and effective, precise signal registration from frame to frame is essential for success. In practical applications such as motion pictures and television, the image may change from frame to frame. Parts of the image may move by translation or rotation, by changing in size or by combinations of the above. In some prior-art systems, frame averaging was only applied to still areas of an image, that is to say, those areas which did not exhibit motion from frame to frame. Other prior-art systems attempted to estimate the movement of in image from one frame to the next and to compensate for this motion in applying frame averaging. In order to perform this motion-compensated image restoration, the image frames are averaged along approximate motion trajectories.
In an article by J. M. Boyce entitled "Noise Reduction of Image Sequences Using Adaptive Motion Compensated Frame Averaging", IEEE ICASSP-92, pages III-461 through III-464, a scheme is proposed to noise reduce image sequences by adaptively switching between simple (non-displaced) frame averaging and motion-compensated frame averaging on a block by block basis. In particular, a method is disclosed to adaptively switch between a displaced frame averaging method (i.e., motion-compensated frame averaging) and a non-displaced averaging (simple frame averaging) based on the relative differences between two blocks which differences are attributable to noise and to motion, respectively. The displaced frame averaging is applied to blocks which contain moving objects and displacement does not take place on blocks for which interframe differences are due only to noise.
An alternative way of accomplishing noise reduction is disclosed by an article by T. J. Dennis entitled, "Nonlinear Temporal Filter For Television Picture Noise Reduction", IEEE Proceedings, Vol 127, Pt. G, No. 2, April 1980, pages 52 et seq. Specifically, a conventional recursive interframe low pass filter for a 625 line 5.5 MHz monochrome television is modified so that any attenuation of frame differences is instantaneously dependent on their amplitude. Thus, the filter will attenuate large area spatial interference, such as streaking, provided it does not contain zero frequency or frame frequency components. Using this method, however, some spatial degradation may occur in areas of the image which contain motion.
A further technique to accomplish noise reduction is disclosed by an article by E. Dubois et al. entitled, "Noise Reduction in Image Sequences Using Motion-Compensated Temporal Filtering," IEEE Transactions on Communications, Vol. COM-32, No. 7, July 1984, pages 826 et seq. In particular, the nonlinear recursive filtering approach is extended by the application of motion-compensation techniques. Furthermore, a specific noise reducer for use with NTSC composite television signals is disclosed. Unlike prior low order non-recursive filters, the nonlinear recursive filtering approach described by Dubois is able to reduce noise to a greater extent than prior art noise reduction systems. However, there are still practical limitations on the ability of the circuit to effectively reduce various types of noise.